Method and apparatus for predicting a number of individuals interested in an item based on recommendations of such item

ABSTRACT

A method ( 800 ) and apparatus ( 100 ) are disclosed for predicting a level of interest in an item, such as the size of an audience for a television program, based on the selection history ( 120 ) of multiple users and the extent to which the item is recommended ( 220 ) to the multiple users. The size of an audience for a given program can be predicted based on, for example, the percentage of users to which the given program is “highly recommended.” A method ( 900 ) for calibrating the accuracy of the predictions using measurement data indicating the actual size of the audience is also disclosed. A comparison of the predicted and actual audiences allows a correction factor to be generated to improve subsequent predictions.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is related to U.S. patent application Ser. No.09/953,385, entitled “Four-Way Recommendation Method and SystemIncluding Collaborative Filtering,” filed Sep. 10, 2001, (AttorneyDocket Number US010128) and U.S. patent application Ser. No. 10/014,194,entitled “Method and Apparatus for Recommending Items of Interest to aUser Based on Recommendations for One or More Third Parties,” filed Nov.13, 2001, (Attorney Docket Number US010571), each incorporated byreference herein.

The present invention relates to methods and apparatus for predicting alevel of interest in an item, such as the size of an audience for atelevision program, and more particularly, to techniques for predictinga number of individuals that will be interested in an item usingrecommendations of the item.

A number of recommendation tools are available that recommend televisionprograms and other items of interest. Television program recommendationtools, for example, typically apply user preferences to an electronicprogram guide (EPG) to obtain a set of recommended programs that may beof interest to one or more users. Electronic program guides identifyavailable television programs, for example, by title, time, date andchannel. Generally, television program recommendation tools obtain thepreferences of a user using implicit or explicit techniques (or both).Implicit television program recommendation tools generate televisionprogram recommendations based on information derived from the viewinghistory of the user. Explicit television program recommendation tools,on the other hand, explicitly question users about their preferences forcertain program attributes, such as title, genre, actors, channel anddate/time, to derive user profiles and generate recommendations.

An explicit recommendation tool must be initialized, requiring each newuser to respond to a very detailed survey specifying their preferencesat a coarse level of granularity. Likewise, implicit television programrecommendation tools require a significant amount of time to learn theuser's viewing preferences. Thus, a recommendation tool is said toexhibit a “cold start” with a new user, since a recommendation tool istypically unable to make valuable recommendations when therecommendation tool is first obtained. The effectiveness of therecommendation tool, however, increases over time as the user interactswith the system.

In order to address the cold start problem, a number of recommendationtools have been proposed or suggested that make recommendations to a newuser based on the viewing history or purchase history of otherindividuals (collectively, a “selection history”) or based onrecommendations that were generated for other individuals. For example,U.S. patent application Ser. No. 10/014,195, entitled “Method andApparatus for Recommending Items of Interest Based on StereotypePreferences of Third Parties,” filed Nov. 13, 2001, (Attorney DocketNumber US010575), incorporated by reference herein, describes arecommendation tool that recommends items of interest to a user, beforea selection history of the user is available. The selection history ofother users are processed to generate stereotype profiles that reflectthe typical patterns of items selected by representative users. A newuser can then select the most relevant stereotype(s) from the generatedstereotype profiles and thereby initialize his or her profile with theitems that are closest to his or her own interests.

In addition to recommending items of interest to a given user, it wouldbe useful to predict a number of individuals that will be interested inan item, such as the size of an audience for a television program.Typically, the audience for a given television program is measuredfollowing a broadcast by determining the television channels that themembers of a given population selected. Nielsen Media Research, forexample, uses a panel of households, often referred to as “NielsenFamilies,” to measure television viewing. Such measurement techniques,however, can only measure the size of the audience for a program thathas already been presented.

A need therefore exists for methods and apparatus for predicting a levelof interest in an item, such as the size of an audience for a televisionprogram. A further need exists for methods and apparatus for predictinga level of interest in an item based on the extent to which the item wasrecommended to potential users.

Generally, a method and apparatus are disclosed for predicting a levelof interest in an item, such as the size of an audience for a televisionprogram, based on the selection history of multiple users and the extentto which the item is recommended to the multiple users. The multipleusers may be, for example, the subscribers of a cable or satellitetelevision service provider in a geographic area. A service provider canpredict the size of an audience for a given program based on thepercentage of its subscribers to which the given program is “highlyrecommended.” In this manner, the granularity of the predictionsgenerated by the present invention can vary from a local area to anational area, in accordance with the geographic scope of thesubscribers. A given program can be considered “highly recommended” to asubscriber, e.g., if the program (i) had a program recommendation scoreexceeding a predefined threshold; or (ii) is in a top-N list ofrecommended programs for the user in a given time interval.

According to another aspect of the invention, a method for calibratingthe accuracy of the predictions using measurement data indicating theactual size of the audience is disclosed. The actual measurement datamay be obtained, for example, from a research firm, a survey, or bymonitoring the actual viewing of the subscribers. A comparison of thepredicted and actual audiences allows a correction factor to begenerated to improve subsequent predictions. In addition, a feedbackmechanism updates the feature counts of a given user, based on the showsthat are actually watched (and optionally, not watched). The accuracy ofthe user recommendations will increase over time as the users interactwith the system. It thus becomes more likely that only a single programis highly recommended for a given user for a given time slot. In thisregard, the predictions will “self correct” as the viewing histories ofthe multiple users increase over time. Thus, the predictions generatedby the present invention will improve over time and can compensate forerrors based on both sampled and unsampled users.

The predictions generated by the present invention can be employed, forexample, by broadcasters to dynamically adjust the price of advertisingbased on the predicted size of an audience. In addition, the generatedpredictions can be employed by advertisers to dynamically adjust thecontent of advertising presented during a given program to appeal to thepredicted audience for the program. A manufacturer of an item or thepublisher of a book or other printed material can use the predictionsprovided by the present invention to determine, for example, how manyitems to manufacture or how many copies of a book to print.

A more complete understanding of the present invention, as well asfurther features and advantages of the present invention, will beobtained by reference to the following detailed description anddrawings.

FIG. 1 is a schematic block diagram of one embodiment of an audiencepredictor in accordance with the present invention;

FIG. 2 is a schematic block diagram of a second embodiment of anaudience predictor in accordance with the present invention;

FIG. 3 is a sample table from the user profile database of FIG. 1;

FIG. 4 is a sample table from the program database of FIGS. 1 and 2;

FIG. 5 is a sample table from the correction factor database of FIGS. 1and 2;

FIG. 6 is a flow chart describing an exemplary profiling process used bythe audience predictor of FIG. 1;

FIG. 7 is a flow chart describing an exemplary program recommendationprocess used by the audience predictor of FIG. 1;

FIG. 8 is a flow chart describing an exemplary audience predictionprocess embodying principles of the present invention and used by theaudience predictor of FIGS. 1 and 2; and

FIG. 9 is a flow chart describing an exemplary prediction biascorrection process embodying principles of the present invention andused by the audience predictor of FIGS. 1 and 2.

Generally, the present invention predicts a level of interest in anitem, such as the size of an audience for a television program, based onthe selection history of multiple users, such as the subscribers of acable or satellite television service provider in a geographic area, andthe extent to which items are recommended to the users. In an exemplaryembodiment, the present invention provides an audience predictor 100 forpredicting the size of an audience for one or more programs. In thismanner, if a service provider in a given geographic region collectsviewing histories or program recommendations from its subscribers, theservice provider can predict the size of an audience for a given programin its coverage area.

FIG. 1, discussed hereinafter, discloses a first embodiment of thepresent invention, where the audience predictor 100 uses the raw viewinghistories of a number of users to predict the size of an audience. FIG.2 discloses a second embodiment of the present invention, where theaudience predictor 200 uses the program recommendations that weregenerated for a number of users to predict the size of an audience.

A service provider can predict the size of an audience for a givenprogram based on the percentage of its subscribers to which the givenprogram is “highly recommended.” A given program can be considered“highly recommended” to a subscriber, e.g., if the program (i) had aprogram recommendation score exceeding a predefined threshold; or (ii)is in a top-N list of recommended programs for the user in a given timeinterval. In a further variation, a given program can be considered“highly recommended” if an average recommendation score based on aplurality of users exceeds a predefined threshold or if the program isat or near the top of the recommended list (by program recommendationscores) and has a predefined gap to the next-most-recommended show.Thus, if a subscriber determines that a given program is “highlyrecommended” to a certain percentage of its subscribers, the subscribercan translate the “highly recommended” percentage to predict the size ofthe audience for the program.

In addition, another aspect provides a method for calibrating theaccuracy of the predictions using actual measurement data indicating thesize of the audience. The actual measurement data may be obtained, forexample, from a research firm, such as Nielsen Media Research or asurvey firm, or by monitoring the actual viewing of the subscribers. Asdiscussed further below, a comparison of the predicted and actualaudiences allows a correction factor to be generated to improvesubsequent predictions. In this manner, the predictions will improveover time and can compensate for errors based on both sampled andunsampled users.

FIG. 1 illustrates one embodiment of an audience predictor 100 inaccordance with the present invention. As shown in FIG. 1, the exemplaryaudience predictor 100 uses the viewing histories 120-1 through 120-N(collectively, the viewing histories 120) of a number of users topredict the size of an audience for one or more programs identified inan electronic program guide (EPG) I 0. The audience predictor I 00 maybe associated, for example, with a central server of a cable orsatellite service provider. In this manner, if a service provider in agiven geographic region collects viewing histories 120 (or programrecommendations 220) from its subscribers, the service provider is ableto predict the size of an audience for a given program in its coveragearea.

The audience predictor 100 can collect the viewing histories 120, forexample, by directly sampling the program choices of each user or byreceiving a viewing history 120 over a network from the set-top terminalor television of each user. The audience predictor 100 can communicatewith the set-top terminal or television of each user in any knownmanner, including one or more wired or wireless links (or both). Whilethe present invention is illustrated herein in the context of televisionprogramming predictions, the present invention can be applied to anyautomatically generated recommendations that are based on an evaluationof user behavior, such as a viewing history or a purchase history.

The audience predictor 100 may be embodied as any computing device, suchas a personal computer or workstation, that contains a processor 150,such as a central processing unit (CPU), and memory 160, such as RAMand/or ROM. The television program recommender 100 may also be embodiedas an application specific integrated circuit (ASIC), for example, in aset-top terminal or display (not shown).

As shown in FIG. 1, and discussed further below in conjunction withFIGS. 2 through 9 respectively, the memory 160 of the audience predictor100 includes a plurality of user profiles 300, a program database 400, acorrection factor database 500, a profiling process 600, a programrecommendation process 700, an audience prediction process 800 and aprediction bias correction process 900. Generally, the illustrative userprofiles 300 provide feature counts derived from the users' viewinghistories 120. The program database 400 records information for eachprogram that is available in a given time interval. The correctionfactor database 500 records a correction factor that is used to correctfor any bias in the predictions generated by the present invention.

The profiling process 600 processes the viewing histories 120 togenerate the corresponding user profiles 300. The program recommendationprocess 700 generates program recommendation scores for the programs ina time period of interest, based on the feature counts in the userprofiles 300. The audience prediction process 800 predicts the size ofan audience for a given television program based on the extent to whichthe program was recommended to the sampled users. The prediction biascorrection process 900 compares the predicted audience and actualaudience for a given program and generates the correction factorsrecorded in the correction factor database 500 and otherwise correctsfor prediction errors.

FIG. 2 illustrates a second embodiment of an audience predictor 200 inaccordance with the present invention. As shown in FIG. 2, the exemplaryaudience predictor 200 uses the program recommendations 220-1 through220-N (collectively, the program recommendations 220) of a number ofusers to predict the size of an audience for one or more programsidentified in an electronic program guide (EPG) 110. The audiencepredictor 200 may be associated, for example, with a central server of acable or satellite service provider and can receive the programrecommendations 220, for example, over a network from the programrecommender, set-top terminal or television of each user.

The program recommendations 220 can be generated for each user, forexample, by any available television program recommender, such as theTivo™ system, commercially available from Tivo, Inc., of Sunnyvale,Calif., or the television program recommenders described in U.S. patentapplication Ser. No. 09/466,406, filed Dec. 17, 1999, entitled “Methodand Apparatus for Recommending Television Programming Using DecisionTrees,” U.S. patent application Ser. No. 09/498,271, filed Feb. 4, 2000,entitled “Bayesian TV Show Recommender,” and U.S. patent applicationSer. No. 09/627,139, filed Jul. 27, 2000, entitled “Three-Way MediaRecommendation Method and System,” or any combination thereof, eachincorporated herein by reference herein.

The program recommendations 220 that are provided to the audiencepredictor 200 may be a top-N list of recommendations for each user, andmay optionally include a recommendation score and an indication ofwhether the user has flagged a given program for recording (whichprovides a strong indicator that the user will watch the program). Theaudience predictor 200 predicts the size of an audience for one or moreprograms that are influenced by the viewing habits of multiple users andthe extent to which programs are recommended to the users.

The audience predictor 200 may be embodied as any computing device, suchas a personal computer or workstation, that contains a processor 250,such as a central processing unit (CPU), and memory 260, such as RAMand/or ROM. The television program recommender 200 may also be embodiedas an application specific integrated circuit (ASIC), for example, in aset-top terminal.

The audience predictor 200 receives program recommendations 220 and notraw viewing histories 120 (like the audience predictor 100). Thus, theaudience predictor 200 does not require the functionality required ofthe audience predictor 100 to process the received viewing histories 120to generate corresponding user profiles 300 and generate recommendationstherefrom. Thus, as shown in FIG. 2, and discussed further below inconjunction with FIGS. 4, 5, 8, and 9 respectively, the memory 260 ofthe audience predictor 200 includes only a program database 400, acorrection factor database 500, an audience prediction process 800 and aprediction bias correction process 900. Thus, the embodiment shown inFIG. 2 has the added benefit that it permits making predictions whileprotecting the privacy (to some extent) of the users by keeping theirviewing histories and user profiles private to their own boxes.

FIG. 3 is a table illustrating an exemplary implicit user profile 300.As shown in FIG. 3, the implicit user profile 300 contains a pluralityof records 305-313 each associated with a different program feature. Inaddition, for each feature set forth in column 330, the implicit userprofile 300 provides corresponding positive counts in fields 335 andnegative counts in field 350. The positive counts indicate the number oftimes the user watched programs having each feature. The negative countsindicate the number of times the user did not watch programs having eachfeature.

For each positive and negative program example (i.e., programs watchedand not watched), a number of program features are classified in theuser profile 300. For example, if a given user watched a given sportsprogram ten times on Channel 2 in the late afternoon, then the positivecounts associated with these features in the implicit user profile 300would be incremented by 10 in field 335, and the negative counts wouldbe 0 (zero). Since the implicit viewing profile 300 is based on theuser's viewing history 120-i, the data contained in the profile 300 isrevised over time, as the viewing history grows. Alternatively, theimplicit user profile 300 can be based on a generic or predefinedprofile, for example, selected for the user based on his or herdemographics.

Although the user profile 300 is illustrated using an implicit userprofile, the user profile 300 may also be embodied using an explicitprofile, or a combination of explicit and implicit profiles, as would beapparent to a person of ordinary skill in the art. For a discussion of atelevision program recommender that employs both implicit and explicitprofiles to obtain a combined program recommendation score, see, forexample, U.S. patent application Ser. No. 09/666,401, filed Sep. 20,2000, entitled “Method And Apparatus For Generating RecommendationScores Using Implicit And Explicit Viewing Preferences,” incorporated byreference herein.

FIG. 4 is a sample table from the program database 400 of FIGS. 1 and 2that records information for each program that is available in a giventime interval. The data that appears in the program database 400 may beobtained, for example, from the electronic program guide 110. As shownin FIG. 4, the program database 400 contains a plurality of records,such as records 405 through 420, each associated with a given program.For each program, the program database 400 indicates the date/time andchannel associated with the program in fields 440 and 445, respectively.In addition, the title and genre for each program are identified infields 450 and 455. Additional well-known attributes (not shown), suchas actors, duration, and description of the program, can also beincluded in the program database 400.

The program database 400 may also optionally record an indication of thepredicted audience as determined by the audience prediction process 800in field 480.

FIG. 5 is a table illustrating an exemplary correction factor database500.

As shown in FIG. 5, the correction factor database 500 contains aplurality of records 510-570 each associated with a different correctionfactor rule. In addition, for each correction factor rule set forth incolumn 580, the correction factor database 500 provides correspondingcorrection factor in field 590. Generally, as discussed further below inconjunction with FIG. 9, the correction factor corrects for biases in agenerated audience prediction.

The exemplary correction factor database 500 is accessed for a givenprogram until a correction factor rule is satisfied. For example, thecorrection factor database 500 can record a correction factor for eachprogram for which an audience was predicted by the audience predictor100, 200 and for which actual audience measurement statistics areavailable. For those programs for which an actual correction factor isnot available, the exemplary correction factor database 500 records acorrection factor that applies to all programs of the same genre.Finally, if no correction factor rule is satisfied by a given program,the default rule in record 570 will apply a default correction factor,such as a correction factor equal to one.

FIG. 6 is a flow chart describing an exemplary profiling process 600. Aspreviously indicated, the profiling process 600 processes the viewinghistories 120 to generate the corresponding user profiles 300.

As shown in FIG. 6, the profiling process 600 initially receives theviewing histories 120 from the plurality of users during step 610.Thereafter, the profiling process 600 updates the user profiles 300during step 620 for each user with the corresponding feature countsbased on the programs that were watched (and optionally, not watched) byeach user.

FIG. 7 is a flow chart describing an exemplary program recommendationprocess 700. As previously indicated, the program recommendation process700 generates program recommendation scores for the programs in a timeperiod of interest, based on the feature counts in the user profiles300. As shown in FIG. 7, the program recommendation process 700initially obtains the electronic program guide (EPG) 110 during step 710for the time period of interest. Thereafter, the program recommendationprocess 700 calculates a program recommendation score, R, during step720 for each sampled user for each program in the time period ofinterest in a conventional manner (or obtains the program recommendationscore, R, from a conventional recommender). The program recommendationscore, R, can optionally be recorded in the program database 400.

The individual program recommendation scores, R, calculated during step720 may be generated, for example, using any known techniques, such asthose employed by the Tivo™ system, commercially available from Tivo,Inc., of Sunnyvale, Calif., or the television program recommendersdescribed in U.S. patent application Ser. No. 09/466,406, filed Dec. 17,1999, entitled “Method and Apparatus for Recommending TelevisionProgramming Using Decision Trees,” U.S. patent application Ser. No.09/498,271, filed Feb. 4, 2000, entitled “Bayesian TV Show Recommender,”and U.S. patent application Ser. No. 09/627,139, filed Jul. 27, 2000,entitled “Three-Way Media Recommendation Method and System,” or anycombination thereof, each incorporated by reference herein.

FIG. 8 is a flow chart describing an exemplary audience predictionprocess 800. As previously indicated, the audience prediction process800 predicts the size of an audience for a given television programbased on the extent to which the program was recommended to the sampledusers. As shown in FIG. 8, the audience prediction process 800 initiallyobtains the individual program recommendation scores, R, for the programfrom the program recommendation process 700 during step 810. Thereafter,the audience prediction process 800 determines the percentage ofsubscribers to which the program was “highly recommended” during step820. As previously indicated, a given program can be considered “highlyrecommended” to a subscriber, e.g., if the program (i) had a programrecommendation score exceeding a predefined threshold; or (ii) is in atop-N list of recommended programs for the user in a given timeinterval. For example, a histogram can be generated during step 820indicating the number of users to which each program was highlyrecommended.

Finally, the audience prediction process 800 predicts the audience forthe program based on the “highly recommended” percentage during step830. In one implementation, the predicted audience is equal to the“highly recommended” percentage (normalized to 100%) multiplied by thecorrection factor for the program (as generated by the prediction biascorrection process 900 and recorded in the correction factor database500).

It is noted that the histogram generated during step 820 will fail toinclude some sampled users in the count at all, if their recommendationsfail to rise to the level of “highly recommended,” and will include somesampled users more than once, if more than one program in a given timeslot is “highly recommended.” In other words, in a given time slot, auser may have zero to many “highly recommended” programs. Generally, theeffectiveness of a recommendation tool increases over time as the userinteracts with the system, and it becomes more likely that only a singleprogram is highly recommended for a given time slot. In this regard, thepredictions will “self correct” as the viewing histories 120 of themultiple users increase over time.

Thus, the audience predictor 100, 200 optionally employs a feedbackfeature to automatically update the feature counts for the users in theviewing histories 120 (incrementing the feature counts for unwatchedprograms for all users with multiple “highly recommended” programs in agiven time slot, and incrementing the feature counts for watchedprograms for all users with no “highly recommended” programs in a giventime slot). The implicit recommender increments all features for allwatched programs regardless of recommendations (and similarly fornot-watched programs). Furthermore, the user may elect to providefeedback on his or her own—telling the system that he or she likes ordislikes particular programs. It is assumed that users will be mostmotivated to give feedback in response to poor recommendations.

FIG. 9 is a flow chart describing an exemplary prediction biascorrection process 900. As previously indicated, the prediction biascorrection process 900 compares the predicted audience and actualaudience for a given program and generates the correction factorsrecorded in the correction factor database 500 and otherwise correctsfor prediction errors. As shown in FIG. 9, the prediction biascorrection process 900 initially obtains the predicted audience for agiven program during step 910. Thereafter, the prediction biascorrection process 900 obtains the actual audience for a given programduring step 920, for example, from a research firm, such as NielsenMedia Research or a survey firm, or by monitoring the actual viewing ofthe subscribers. Finally, the current correction factor for the programis adjusted during step 930 by a predefined percentage (such as 10%) ofthe difference between the predicted audience and the actual audience.For example, if a predicted audience for a given program is 20% and theactual audience was 30%, then an initial correction factor of 1.0 wouldbe adjusted by 10% of the difference to provide a new correction factorof 1.01 (1.0+(10%*10%)=1.01) It is noted that a program not previouslyprocessed by the prediction bias correction process 900 will have acorrection factor of one. The new correction factor, if any, is recordedfor the program in the correction factor database 500 during step 940.

It is to be understood that the embodiments and variations shown anddescribed herein are merely illustrative of the principles of thisinvention and that various modifications may be implemented by thoseskilled in the art without departing from the scope and spirit of theinvention.

1. A method for predicting a level of interest in an available item,comprising the steps of: obtaining one or more recommendation scores forsaid available item based on a history of selecting said available itemby a plurality of individuals; and predicting a level of interest insaid available item based on said one or more recommendation scores. 2.The method of claim 1, wherein said one or more recommendation scoresfor said available item is a unique recommendation score for each ofsaid plurality of individuals.
 3. The method of claim 1, wherein saidone or more recommendation scores for said available item is anaggregate recommendation score for said plurality of individuals.
 4. Themethod of claim 1, wherein said obtaining step further comprises thestep of averaging a plurality of recommendation scores for saidavailable item.
 5. The method of claim 1, wherein said obtaining stepfurther comprises the step of receiving said one or more recommendationscores from at least one remote recommender.
 6. The method of claim 1,wherein said obtaining step further comprises the step of receiving saidhistory of selecting from at least one remote recommender.
 7. The methodof claim 1, further comprising the step of comparing said predictedlevel of interest to an actual level of interest and generating acorrection factor to compensate for errors in said predicted level ofinterest.
 8. The method of claim 1, further comprising the step ofupdating said history of selecting based on whether said available itemwas actually selected by at least one of said plurality of individuals.9. The method of claim 1, wherein said available item is a program andsaid level of interest is a size of an audience for said program. 10.The method of claim 1, wherein said available item is content and saidlevel of interest is a size of an audience for said content.
 11. Themethod of claim 1, wherein said available item is a product and saidlevel of interest is a number of customers who will purchase saidproduct.
 12. The method of claim 1, wherein said plurality ofindividuals are subscribers of a service provider in one or moregeographic areas.
 13. The method of claim 1, wherein said level ofinterest is based on a percentage of said plurality of individuals towhich said available item is highly recommended.
 14. The method of claim13, wherein an available item is highly recommended if the item had arecommendation score exceeding a predefined threshold.
 15. The method ofclaim 13, wherein an available item is highly recommended if the item isin a top-N list of recommended items for at least one of said pluralityof individuals.
 16. The method of claim 1, further comprising the stepof adjusting a price of advertising associated with said item based onsaid predicted level of interest.
 17. The method of claim 1, furthercomprising the step of adjusting a content of advertising associatedwith said item based on demographic information of individuals who arepredicted to be interested in said item.
 18. The method of claim 1,further comprising the step of determining a number of said items toproduce based on said predicted level of interest.
 19. An apparatus forpredicting a level of interest in an available item, comprising: amemory; and at least one processor, coupled to the memory, operative to:obtain one or more recommendation scores for said available item basedon a history of selecting said available item by a plurality ofindividuals; and predict a level of interest in said available itembased on said one or more recommendation scores.
 20. The apparatus ofclaim 19, wherein said processor is further configured to compare saidpredicted level of interest to an actual level of interest and generatea correction factor to compensate for errors in said predicted level ofinterest.
 21. The apparatus of claim 19, wherein said processor isfurther configured to update said history of selecting based on whethersaid available item was actually selected by at least one of saidplurality of individuals.
 22. The apparatus of claim 19, wherein saidavailable item is a program and said level of interest is a size of anaudience for said program.
 23. The apparatus of claim 19, wherein saidlevel of interest is based on a percentage of said plurality ofindividuals to which said available item is highly recommended.
 24. Theapparatus of claim 23, wherein an available item is highly recommendedif the item had a recommendation score exceeding a predefined threshold.25. The apparatus of claim 23, wherein an available item is highlyrecommended if the item is in a top-N list of recommended items for atleast one of said plurality of individuals.
 26. The apparatus of claim19, wherein said processor is further configured to adjust a price ofadvertising associated with said item based on said predicted level ofinterest.
 27. The apparatus of claim 19, wherein said processor isfurther configured to adjust content of advertising associated with saiditem based on demographic information of individuals who are predictedto be interested in said item.
 28. An article of manufacture forpredicting a level of interest in an available item, comprising: amachine readable medium containing one or more programs which whenexecuted implement the steps of: obtaining one or more recommendationscores for said available item based on a history of selecting saidavailable item by a plurality of individuals; and predicting a level ofinterest in said available item based on said one or more recommendationscores.